Advancing model risk management: How to break down barriers to innovation?

Resistance to change, talent, compatibility, integrity, and teamwork… Jarrad Hee, Managing Director and Group Head of Capital and Portfolio Analytics at Emirates NBD, explores key leadership challenges in adopting advanced model risk management practices.

Model risk management is evolving rapidly due to advances in AI, machine learning, neural networks, and increasing model complexity under greater regulatory scrutiny. With this in mind, addressing people challenges and change management will be key in adopting more advanced frameworks.

To assess model risk maturity, Jarrad recommends conducting an objective gap analysis – comparing an organisation’s practices to international good practices and regulatory expectations, ideally led by experienced practitioners. Looking ahead, he says risk managers must adapt faster, map new technologies across the model lifecycle, embrace emerging applications, and re-engineer end-to-end processes to realise benefits.

Leadership challenges in model risk management

Leadership challenges in model risk management are multifaceted, touching various aspects such as organisational, regulatory, and most significantly, people-related issues. From Jarrad’s perspective, focusing on the people aspect is crucial as it universally impacts most financial institutions. Once the people challenges are addressed, many issues in model risk management tend to resolve naturally.

Key people challenges include resistance to change, talent alignment, integrity, and teamwork. These elements demand significant attention as part of the organisational framework. With the right people onboard, discussions shift towards how to achieve success collectively rather than tackling individual expectations.

When experienced and capable individuals collaborate, challenges in leadership and model management are addressed more objectively. This enables a focus on overall improvements rather than individual preferences, leading to more effective model management.

Assessing model risk management maturity

The evolving role of model risk management, especially with advancements in artificial intelligence and machine learning, necessitates a new level of business maturity. Jarrad emphasises the importance of conducting a gap analysis to assess maturity levels. This involves comparing existing practices within an organisation against international best practices, often guided by experienced practitioners from mature industries. Such an assessment provides clarity on regulatory expectations versus current organisational practices, allowing institutions to measure and enhance their model maturity effectively.

The future role of risk managers

As technological advancements continue to redefine financial sectors, risk managers must evolve rapidly to stay aligned with changes in analytical methodologies. Jarrad outlines that the life cycle of any model spans from inception to obsolescence, with new technologies influencing various stages differently.

Risk managers need to integrate these technologies, understanding that some aspects are still developing. By embracing these advancements and combining them with practical experience, risk managers can adapt and thrive. This involves re-engineering existing processes rather than simply appending new solutions to outdated methods. Such thorough process re-evaluation and adaptation promise substantial benefits from modern technologies in model risk management.

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